Publications: Inductive Logic Programming

Inductive logic programming (ILP) studies the learning of (Prolog) logic
programs and other relational knowledge from examples. Most machine learning
algorithms are restricted to finite, propositional, feature-based
representations of examples and concepts and cannot learn complex relational
and recursive knowledge. ILP allows learning with much richer representations.
Our work has focussed on applications of ILP to various problems in natural language and theory
refinement for logic programs.

Integrating EBL and ILP to Acquire Control Rules for Planning[Details] [PDF] Tara A. Estlin and Raymond J. MooneyIn Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), 271--279, Harpers Ferry, WV, May 1996.

Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers[Details] [PDF] John M. ZellePhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995.

A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction[Details] [PDF] John M. Zelle and Raymond J. MooneyIn Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, 79--86, Montreal, Quebec, Canada, August 1995.